Bibliographic record
Abstract
INTRODUCTION: Newer implantable pulse generators have data storage capabilities that permit detection of multiple episodes of atrial fibrillation (AF). This study evaluated the clinical predictors and time course of AF development in a general pacemaker population. METHODS AND RESULTS: Patients (n = 231) received DDDR pacemakers with features that permit detection and storage of information about the date, time of onset, and duration of multiple, sequential episodes of AF. Patients were followed for 718+/-383 days. Time to first occurrence of AF, interval between first and second episode of AF, and total AF burden were determined at each follow-up visit. AF occurred more often in patients (68%) with sinus node disease than in patients with AV block (37%; P < 0.001). Time to first occurrence of AF was 21.2 days (95% confidence interval [CI] 14.7 to 30.6 days) after pacemaker implantation. AF burden initially decreased significantly in patients (0.8 hours/day, 95% CI 0.7 to 0.9 at 8 weeks after implant vs 0.6 hours/day, 95% CI 0.4 to 0.8 at 12 months after implant; P = 0.005) but then increased significantly during long-term follow-up (2.0 hours/day, 95% CI 1.0 to 3.7 at 48 months after implant; P = 0.008). The long-term increase in AF burden was seen predominantly in patients with sinus node disease. A prior history of AF and the duration of follow-up were independent predictors of AF occurrence. CONCLUSION: AF develops frequently after dual-chamber pacemaker implantation. AF burden increases progressively over the long term.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".